This chapter provides a thorough examination of sample selection methods indispensable for rigorous management research. It initiates by defining the concept of population and sample size, demonstrating statistical formulas for sample determination under conditions of both finite and infinite populations. The chapter describes key sampling techniques, categorising between probability methods—such as simple random, systematic, stratified, cluster, and multi-stage sampling—and non-probability approaches, comprising quota, purposive, snowball, and convenience sampling. Each method is contextualised with practical examples relevant to organisational research settings, emphasising their advantages, limitations, and implications for representativeness and generalisability. The chapter also addresses contemporary advancements, notably the integration of Artificial Intelligence enhancing sample precision through the identification of elusive populations and simulation of sampling strategies. Whilst distinguishing the innovative potential of AI, the text underscores the need for critical oversight to mitigate algorithmic biases and preserve inclusivity. In sum, this chapter equips researchers with foundational and sophisticated tools to design effective sampling strategies that balance methodological rigour with practical constraints.